Work place: Department of CSE, Ramkrishna Mahato Government Engineering College, Purulia, 723103, India
E-mail: bikashchoudhury@rkmgec.ac.in
Website:
Research Interests:
Biography
Bikash Choudhury received the B.Tech. degree from WBUT, India, and the M.Tech. and Ph.D. degrees from the National Institute of Technology Durgapur, India. He is currently an Assistant Professor with the Department of Computer Science and Engineering, Ramkrishna Mahato Government Engineering College, Purulia, India. His research interests include distributed service-oriented computing, Resource Management in the Internet of Things, and Tiny Machine Learning.
By Mrinal Kanti Mahato Bikash Choudhury Tanushree Garai Sudip Kumar Adhikari Himadri Nath Saha
DOI: https://doi.org/10.5815/ijcnis.2026.04.06, Pub. Date: 8 Aug. 2026
The rapid evolution of the Internet of Things (IoT), supported by the convergence of cloud, edge and mist computing layers, opens new avenues for delivering reliable and responsive services to distributed smart devices. However, ensuring efficient and adaptive service replication in such resource-constrained and dynamically changing IoT environments remains a significant challenge. To tackle this, we introduce Elastic Context-Aware Replication (ECAR), an intelligent replication strategy tailored for IoT systems. ECAR dynamically redistributes services across the IoT continuum by leveraging both physical and logical contextual information. Unlike traditional replication schemes, ECAR continuously adapts to real-time workload fluctuations and network conditions, ensuring low latency and efficient resource usage. ECAR’s effectiveness is demonstrated through a comparative evaluation involving diverse IoT deployment scenarios, including Cloud-Intensive Replication (CIR), Cloud-Edge-Intensive Replication (CEIR) and Cloud-Edge-Access-Intensive Replication (CEAIR), alongside two existing replication strategies, Group-Delay-Aware Replication (GDAR) and Combined Context-Aware Replication (CCA). The evaluation shows ECAR achieving up to 18% reduction in service drop rates, 82% improvement in allocation efficiency and 82% better resource utilization. These results underline ECAR’s effectiveness in supporting scalable, reliable and latency-aware service delivery for IoT deployments.
[...] Read more.Subscribe to receive issue release notifications and newsletters from MECS Press journals